Petrophysical data prediction from seismic attributes using committee fuzzy inference system

Authors:
Ali Kadkhodaie-Ilkhchi;M. Reza Rezaee;Hossain Rahimpour-Bonab;Ali Chehrazi
Affiliations:
Department of Geology, College of Science, University of Tehran, Tehran, Iran;Department of Geology, College of Science, University of Tehran, Tehran, Iran and Department of Petroleum Engineering, Curtin University of Technology, ARRC Building, 26 Dick Perry Avenue, Kensing ...;Department of Geology, College of Science, University of Tehran, Tehran, Iran;Department of Geology, College of Science, University of Tehran, Tehran, Iran and Geology Division, Iranian Offshore Oilfields Company, No. 38, Tooraj St., Vali-Asr Ave., NIOC, Tehran 19395, Iran
Venue:
Computers & Geosciences
Year:
2009

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Abstract

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S"w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.